Overview

Dataset statistics

Number of variables23
Number of observations6566
Missing cells33976
Missing cells (%)22.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory184.0 B

Variable types

Numeric1
Text8
Categorical10
Unsupported4

Alerts

NIVEL has constant value ""Constant
Unnamed: 0 is highly overall correlated with DEPARTAMENTO and 2 other fieldsHigh correlation
DEPARTAMENTO is highly overall correlated with Unnamed: 0 and 2 other fieldsHigh correlation
JORNADA is highly overall correlated with PLANHigh correlation
PLAN is highly overall correlated with JORNADAHigh correlation
DEPARTAMENTAL is highly overall correlated with Unnamed: 0 and 2 other fieldsHigh correlation
ZONA is highly overall correlated with Unnamed: 0 and 2 other fieldsHigh correlation
SECTOR is highly imbalanced (65.6%)Imbalance
AREA is highly imbalanced (59.2%)Imbalance
STATUS is highly imbalanced (51.6%)Imbalance
MODALIDAD is highly imbalanced (83.4%)Imbalance
PLAN is highly imbalanced (55.1%)Imbalance
DISTRITO has 163 (2.5%) missing valuesMissing
TELEFONO has 901 (13.7%) missing valuesMissing
SUPERVISOR has 163 (2.5%) missing valuesMissing
DIRECTOR has 1413 (21.5%) missing valuesMissing
CODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLAN DEPARTA has 6566 (100.0%) missing valuesMissing
CODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLAN has 6566 (100.0%) missing valuesMissing
CODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLAN has 6566 (100.0%) missing valuesMissing
CODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLAN DE has 6566 (100.0%) missing valuesMissing
ZONA has 5030 (76.6%) missing valuesMissing
Unnamed: 0 has unique valuesUnique
CODIGO has unique valuesUnique
CODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLAN DEPARTA is an unsupported type, check if it needs cleaning or further analysisUnsupported
CODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLAN is an unsupported type, check if it needs cleaning or further analysisUnsupported
CODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLAN is an unsupported type, check if it needs cleaning or further analysisUnsupported
CODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLAN DE is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-07-31 22:27:47.865968
Analysis finished2023-07-31 22:27:53.201520
Duration5.34 seconds
Software versionydata-profiling vv4.3.2
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6566
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4095.4781
Minimum0
Maximum15983
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2023-07-31T16:27:53.417257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile328.25
Q11643.25
median3286.5
Q34939.75
95-th percentile15650.75
Maximum15983
Range15983
Interquartile range (IQR)3296.5

Descriptive statistics

Standard deviation3921.1605
Coefficient of variation (CV)0.95743657
Kurtosis3.7299514
Mean4095.4781
Median Absolute Deviation (MAD)1648.5
Skewness2.051825
Sum26890909
Variance15375499
MonotonicityStrictly increasing
2023-07-31T16:27:53.625562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
4385 1
 
< 0.1%
4395 1
 
< 0.1%
4394 1
 
< 0.1%
4393 1
 
< 0.1%
4392 1
 
< 0.1%
4391 1
 
< 0.1%
4390 1
 
< 0.1%
4389 1
 
< 0.1%
4388 1
 
< 0.1%
Other values (6556) 6556
99.8%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
15983 1
< 0.1%
15982 1
< 0.1%
15981 1
< 0.1%
15980 1
< 0.1%
15979 1
< 0.1%
15978 1
< 0.1%
15977 1
< 0.1%
15976 1
< 0.1%
15975 1
< 0.1%
15974 1
< 0.1%

CODIGO
Text

UNIQUE 

Distinct6566
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
2023-07-31T16:27:54.136621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters85358
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6566 ?
Unique (%)100.0%

Sample

1st row16-01-0138-46
2nd row16-01-0139-46
3rd row16-01-0140-46
4th row16-01-0141-46
5th row16-01-0142-46
ValueCountFrequency (%)
16-01-0138-46 1
 
< 0.1%
16-01-0557-46 1
 
< 0.1%
16-01-0143-46 1
 
< 0.1%
16-01-0145-46 1
 
< 0.1%
16-01-0147-46 1
 
< 0.1%
16-01-0150-46 1
 
< 0.1%
16-01-0155-46 1
 
< 0.1%
16-01-0428-46 1
 
< 0.1%
16-01-0471-46 1
 
< 0.1%
16-01-0710-46 1
 
< 0.1%
Other values (6556) 6556
99.8%
2023-07-31T16:27:54.696069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 19698
23.1%
0 18982
22.2%
1 10032
11.8%
4 9228
10.8%
6 9086
10.6%
2 4507
 
5.3%
3 3152
 
3.7%
5 3080
 
3.6%
8 2944
 
3.4%
7 2486
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 65660
76.9%
Dash Punctuation 19698
 
23.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18982
28.9%
1 10032
15.3%
4 9228
14.1%
6 9086
13.8%
2 4507
 
6.9%
3 3152
 
4.8%
5 3080
 
4.7%
8 2944
 
4.5%
7 2486
 
3.8%
9 2163
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 19698
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 85358
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 19698
23.1%
0 18982
22.2%
1 10032
11.8%
4 9228
10.8%
6 9086
10.6%
2 4507
 
5.3%
3 3152
 
3.7%
5 3080
 
3.6%
8 2944
 
3.4%
7 2486
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 19698
23.1%
0 18982
22.2%
1 10032
11.8%
4 9228
10.8%
6 9086
10.6%
2 4507
 
5.3%
3 3152
 
3.7%
5 3080
 
3.6%
8 2944
 
3.4%
7 2486
 
2.9%

DISTRITO
Text

MISSING 

Distinct443
Distinct (%)6.9%
Missing163
Missing (%)2.5%
Memory size51.4 KiB
2023-07-31T16:27:55.259751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9854756
Min length3

Characters and Unicode

Total characters38325
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)0.8%

Sample

1st row16-031
2nd row16-031
3rd row16-031
4th row16-005
5th row16-005
ValueCountFrequency (%)
01-403 242
 
3.8%
05-033 159
 
2.5%
01-411 150
 
2.3%
18-008 128
 
2.0%
01-409 102
 
1.6%
05-007 100
 
1.6%
18-039 98
 
1.5%
13-004 92
 
1.4%
10-019 91
 
1.4%
01-641 87
 
1.4%
Other values (433) 5154
80.5%
2023-07-31T16:27:55.969581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11029
28.8%
1 7785
20.3%
- 6403
16.7%
2 2934
 
7.7%
3 2565
 
6.7%
4 1910
 
5.0%
6 1553
 
4.1%
5 1409
 
3.7%
8 1020
 
2.7%
9 945
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31922
83.3%
Dash Punctuation 6403
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11029
34.5%
1 7785
24.4%
2 2934
 
9.2%
3 2565
 
8.0%
4 1910
 
6.0%
6 1553
 
4.9%
5 1409
 
4.4%
8 1020
 
3.2%
9 945
 
3.0%
7 772
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 6403
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38325
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11029
28.8%
1 7785
20.3%
- 6403
16.7%
2 2934
 
7.7%
3 2565
 
6.7%
4 1910
 
5.0%
6 1553
 
4.1%
5 1409
 
3.7%
8 1020
 
2.7%
9 945
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38325
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11029
28.8%
1 7785
20.3%
- 6403
16.7%
2 2934
 
7.7%
3 2565
 
6.7%
4 1910
 
5.0%
6 1553
 
4.1%
5 1409
 
3.7%
8 1020
 
2.7%
9 945
 
2.5%

DEPARTAMENTO
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
GUATEMALA
2970 
ESCUINTLA
599 
HUEHUETENANGO
495 
SUCHITEPEQUEZ
377 
IZABAL
360 
Other values (9)
1765 

Length

Max length13
Median length9
Mean length9.6749924
Min length6

Characters and Unicode

Total characters63526
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALTA VERAPAZ
2nd rowALTA VERAPAZ
3rd rowALTA VERAPAZ
4th rowALTA VERAPAZ
5th rowALTA VERAPAZ

Common Values

ValueCountFrequency (%)
GUATEMALA 2970
45.2%
ESCUINTLA 599
 
9.1%
HUEHUETENANGO 495
 
7.5%
SUCHITEPEQUEZ 377
 
5.7%
IZABAL 360
 
5.5%
CHIMALTENANGO 349
 
5.3%
ALTA VERAPAZ 348
 
5.3%
JUTIAPA 320
 
4.9%
CHIQUIMULA 172
 
2.6%
JALAPA 149
 
2.3%
Other values (4) 427
 
6.5%

Length

2023-07-31T16:27:56.240191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
guatemala 2970
41.5%
escuintla 599
 
8.4%
huehuetenango 495
 
6.9%
verapaz 468
 
6.5%
suchitepequez 377
 
5.3%
izabal 360
 
5.0%
chimaltenango 349
 
4.9%
alta 348
 
4.9%
jutiapa 320
 
4.5%
chiquimula 172
 
2.4%
Other values (6) 698
 
9.8%

Most occurring characters

ValueCountFrequency (%)
A 15018
23.6%
E 7246
11.4%
U 5977
 
9.4%
T 5638
 
8.9%
L 5069
 
8.0%
G 3936
 
6.2%
M 3491
 
5.5%
N 2467
 
3.9%
I 2439
 
3.8%
H 1888
 
3.0%
Other values (11) 10357
16.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 62936
99.1%
Space Separator 590
 
0.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15018
23.9%
E 7246
11.5%
U 5977
 
9.5%
T 5638
 
9.0%
L 5069
 
8.1%
G 3936
 
6.3%
M 3491
 
5.5%
N 2467
 
3.9%
I 2439
 
3.9%
H 1888
 
3.0%
Other values (10) 9767
15.5%
Space Separator
ValueCountFrequency (%)
590
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 62936
99.1%
Common 590
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15018
23.9%
E 7246
11.5%
U 5977
 
9.5%
T 5638
 
9.0%
L 5069
 
8.1%
G 3936
 
6.3%
M 3491
 
5.5%
N 2467
 
3.9%
I 2439
 
3.9%
H 1888
 
3.0%
Other values (10) 9767
15.5%
Common
ValueCountFrequency (%)
590
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63526
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15018
23.6%
E 7246
11.4%
U 5977
 
9.4%
T 5638
 
8.9%
L 5069
 
8.0%
G 3936
 
6.2%
M 3491
 
5.5%
N 2467
 
3.9%
I 2439
 
3.8%
H 1888
 
3.0%
Other values (11) 10357
16.3%

MUNICIPIO
Text

Distinct189
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
2023-07-31T16:27:56.784332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length27
Median length23
Mean length12.126866
Min length5

Characters and Unicode

Total characters79625
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowCOBAN
2nd rowCOBAN
3rd rowCOBAN
4th rowCOBAN
5th rowCOBAN
ValueCountFrequency (%)
ciudad 1536
 
13.5%
capital 1536
 
13.5%
san 900
 
7.9%
villa 431
 
3.8%
mixco 420
 
3.7%
nueva 400
 
3.5%
santa 251
 
2.2%
chimaltenango 170
 
1.5%
mazatenango 167
 
1.5%
escuintla 164
 
1.4%
Other values (211) 5433
47.6%
2023-07-31T16:27:57.655390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 15200
19.1%
I 6708
 
8.4%
C 6068
 
7.6%
T 5052
 
6.3%
L 5019
 
6.3%
N 4964
 
6.2%
4842
 
6.1%
U 4830
 
6.1%
E 3967
 
5.0%
O 3440
 
4.3%
Other values (15) 19535
24.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 74783
93.9%
Space Separator 4842
 
6.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15200
20.3%
I 6708
9.0%
C 6068
 
8.1%
T 5052
 
6.8%
L 5019
 
6.7%
N 4964
 
6.6%
U 4830
 
6.5%
E 3967
 
5.3%
O 3440
 
4.6%
D 3392
 
4.5%
Other values (14) 16143
21.6%
Space Separator
ValueCountFrequency (%)
4842
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 74783
93.9%
Common 4842
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15200
20.3%
I 6708
9.0%
C 6068
 
8.1%
T 5052
 
6.8%
L 5019
 
6.7%
N 4964
 
6.6%
U 4830
 
6.5%
E 3967
 
5.3%
O 3440
 
4.6%
D 3392
 
4.5%
Other values (14) 16143
21.6%
Common
ValueCountFrequency (%)
4842
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15200
19.1%
I 6708
 
8.4%
C 6068
 
7.6%
T 5052
 
6.3%
L 5019
 
6.3%
N 4964
 
6.2%
4842
 
6.1%
U 4830
 
6.1%
E 3967
 
5.0%
O 3440
 
4.3%
Other values (15) 19535
24.5%

ESTABLECIMIENTO
Text

Distinct3526
Distinct (%)53.7%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
2023-07-31T16:27:58.175931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length125
Median length103
Mean length39.777947
Min length3

Characters and Unicode

Total characters261182
Distinct characters49
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2260 ?
Unique (%)34.4%

Sample

1st rowCOLEGIO COBAN
2nd rowCOLEGIO PARTICULAR MIXTO VERAPAZ
3rd rowCOLEGIO "LA INMACULADA"
4th rowESCUELA NACIONAL DE CIENCIAS COMERCIALES
5th rowINSTITUTO NORMAL MIXTO DEL NORTE 'EMILIO ROSALES PONCE'
ValueCountFrequency (%)
de 2657
 
7.7%
colegio 2495
 
7.3%
mixto 1922
 
5.6%
instituto 1769
 
5.1%
liceo 1347
 
3.9%
educacion 976
 
2.8%
privado 954
 
2.8%
centro 842
 
2.4%
diversificada 547
 
1.6%
y 540
 
1.6%
Other values (2443) 20358
59.2%
2023-07-31T16:27:58.929346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27849
10.7%
I 27322
10.5%
O 25757
9.9%
E 23018
 
8.8%
A 22252
 
8.5%
C 18444
 
7.1%
T 16064
 
6.2%
N 15063
 
5.8%
L 12539
 
4.8%
R 11954
 
4.6%
Other values (39) 60920
23.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 228339
87.4%
Space Separator 27849
 
10.7%
Other Punctuation 3854
 
1.5%
Dash Punctuation 468
 
0.2%
Decimal Number 344
 
0.1%
Open Punctuation 163
 
0.1%
Close Punctuation 162
 
0.1%
Modifier Symbol 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 27322
12.0%
O 25757
11.3%
E 23018
10.1%
A 22252
9.7%
C 18444
 
8.1%
T 16064
 
7.0%
N 15063
 
6.6%
L 12539
 
5.5%
R 11954
 
5.2%
D 9555
 
4.2%
Other values (16) 46371
20.3%
Decimal Number
ValueCountFrequency (%)
2 125
36.3%
0 70
20.3%
1 54
15.7%
3 31
 
9.0%
4 19
 
5.5%
7 15
 
4.4%
6 11
 
3.2%
8 7
 
2.0%
9 6
 
1.7%
5 6
 
1.7%
Other Punctuation
ValueCountFrequency (%)
" 2336
60.6%
. 719
 
18.7%
' 707
 
18.3%
, 77
 
2.0%
& 7
 
0.2%
/ 6
 
0.2%
% 1
 
< 0.1%
# 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
27849
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 468
100.0%
Open Punctuation
ValueCountFrequency (%)
( 163
100.0%
Close Punctuation
ValueCountFrequency (%)
) 162
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 228339
87.4%
Common 32843
 
12.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 27322
12.0%
O 25757
11.3%
E 23018
10.1%
A 22252
9.7%
C 18444
 
8.1%
T 16064
 
7.0%
N 15063
 
6.6%
L 12539
 
5.5%
R 11954
 
5.2%
D 9555
 
4.2%
Other values (16) 46371
20.3%
Common
ValueCountFrequency (%)
27849
84.8%
" 2336
 
7.1%
. 719
 
2.2%
' 707
 
2.2%
- 468
 
1.4%
( 163
 
0.5%
) 162
 
0.5%
2 125
 
0.4%
, 77
 
0.2%
0 70
 
0.2%
Other values (13) 167
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 261182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27849
10.7%
I 27322
10.5%
O 25757
9.9%
E 23018
 
8.8%
A 22252
 
8.5%
C 18444
 
7.1%
T 16064
 
6.2%
N 15063
 
5.8%
L 12539
 
4.8%
R 11954
 
4.6%
Other values (39) 60920
23.3%

DIRECCION
Text

Distinct4216
Distinct (%)64.6%
Missing42
Missing (%)0.6%
Memory size51.4 KiB
2023-07-31T16:27:59.217227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length110
Median length90
Mean length29.358063
Min length4

Characters and Unicode

Total characters191532
Distinct characters49
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3113 ?
Unique (%)47.7%

Sample

1st rowKM.2 SALIDA A SAN JUAN CHAMELCO ZONA 8
2nd rowKM 209.5 ENTRADA A LA CIUDAD
3rd row7A. AVENIDA 11-109 ZONA 6
4th row2A CALLE 11-10 ZONA 2
5th row3A AVE 6-23 ZONA 11
ValueCountFrequency (%)
zona 2829
 
7.9%
calle 2158
 
6.0%
avenida 1682
 
4.7%
1 1217
 
3.4%
colonia 931
 
2.6%
barrio 810
 
2.3%
aldea 693
 
1.9%
san 662
 
1.8%
el 641
 
1.8%
la 443
 
1.2%
Other values (2879) 23864
66.4%
2023-07-31T16:27:59.715262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29406
15.4%
A 27517
14.4%
E 12045
 
6.3%
L 11999
 
6.3%
O 11429
 
6.0%
N 11168
 
5.8%
I 8816
 
4.6%
C 7408
 
3.9%
R 6805
 
3.6%
1 5391
 
2.8%
Other values (39) 59548
31.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 128153
66.9%
Space Separator 29406
 
15.4%
Decimal Number 23603
 
12.3%
Other Punctuation 6355
 
3.3%
Dash Punctuation 3966
 
2.1%
Lowercase Letter 17
 
< 0.1%
Open Punctuation 16
 
< 0.1%
Close Punctuation 16
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 27517
21.5%
E 12045
9.4%
L 11999
9.4%
O 11429
8.9%
N 11168
8.7%
I 8816
 
6.9%
C 7408
 
5.8%
R 6805
 
5.3%
D 4594
 
3.6%
T 4250
 
3.3%
Other values (16) 22122
17.3%
Decimal Number
ValueCountFrequency (%)
1 5391
22.8%
2 3213
13.6%
3 2777
11.8%
4 2438
10.3%
5 2230
9.4%
0 1943
 
8.2%
6 1732
 
7.3%
7 1486
 
6.3%
9 1207
 
5.1%
8 1186
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 3732
58.7%
, 2109
33.2%
" 383
 
6.0%
' 86
 
1.4%
/ 29
 
0.5%
# 15
 
0.2%
; 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 16
94.1%
o 1
 
5.9%
Space Separator
ValueCountFrequency (%)
29406
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3966
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 128170
66.9%
Common 63362
33.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 27517
21.5%
E 12045
9.4%
L 11999
9.4%
O 11429
8.9%
N 11168
8.7%
I 8816
 
6.9%
C 7408
 
5.8%
R 6805
 
5.3%
D 4594
 
3.6%
T 4250
 
3.3%
Other values (18) 22139
17.3%
Common
ValueCountFrequency (%)
29406
46.4%
1 5391
 
8.5%
- 3966
 
6.3%
. 3732
 
5.9%
2 3213
 
5.1%
3 2777
 
4.4%
4 2438
 
3.8%
5 2230
 
3.5%
, 2109
 
3.3%
0 1943
 
3.1%
Other values (11) 6157
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 191532
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29406
15.4%
A 27517
14.4%
E 12045
 
6.3%
L 11999
 
6.3%
O 11429
 
6.0%
N 11168
 
5.8%
I 8816
 
4.6%
C 7408
 
3.9%
R 6805
 
3.6%
1 5391
 
2.8%
Other values (39) 59548
31.1%

TELEFONO
Text

MISSING 

Distinct3382
Distinct (%)59.7%
Missing901
Missing (%)13.7%
Memory size51.4 KiB
2023-07-31T16:27:59.987995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters45320
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2237 ?
Unique (%)39.5%

Sample

1st row77945104
2nd row77367402
3rd row78232301
4th row79514215
5th row79521468
ValueCountFrequency (%)
22067425 21
 
0.4%
79480009 14
 
0.2%
22093200 12
 
0.2%
77746400 11
 
0.2%
45353648 11
 
0.2%
59304894 11
 
0.2%
24637777 10
 
0.2%
78899679 10
 
0.2%
22322912 10
 
0.2%
78394519 9
 
0.2%
Other values (3374) 5549
97.9%
2023-07-31T16:28:00.325306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 6312
13.9%
4 5266
11.6%
7 4868
10.7%
3 4690
10.3%
5 4667
10.3%
0 4117
9.1%
8 4093
9.0%
6 3917
8.6%
9 3733
8.2%
1 3622
8.0%
Other values (6) 35
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45285
99.9%
Dash Punctuation 18
 
< 0.1%
Other Punctuation 8
 
< 0.1%
Space Separator 7
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 6312
13.9%
4 5266
11.6%
7 4868
10.7%
3 4690
10.4%
5 4667
10.3%
0 4117
9.1%
8 4093
9.0%
6 3917
8.6%
9 3733
8.2%
1 3622
8.0%
Other Punctuation
ValueCountFrequency (%)
, 7
87.5%
/ 1
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
E 1
50.0%
Y 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45318
> 99.9%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 6312
13.9%
4 5266
11.6%
7 4868
10.7%
3 4690
10.3%
5 4667
10.3%
0 4117
9.1%
8 4093
9.0%
6 3917
8.6%
9 3733
8.2%
1 3622
8.0%
Other values (4) 33
 
0.1%
Latin
ValueCountFrequency (%)
E 1
50.0%
Y 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 6312
13.9%
4 5266
11.6%
7 4868
10.7%
3 4690
10.3%
5 4667
10.3%
0 4117
9.1%
8 4093
9.0%
6 3917
8.6%
9 3733
8.2%
1 3622
8.0%
Other values (6) 35
 
0.1%

SUPERVISOR
Text

MISSING 

Distinct417
Distinct (%)6.5%
Missing163
Missing (%)2.5%
Memory size51.4 KiB
2023-07-31T16:28:00.579981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length63
Median length43
Mean length29.576136
Min length14

Characters and Unicode

Total characters189376
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)0.6%

Sample

1st rowMERCEDES JOSEFINA TORRES GALVEZ
2nd rowMERCEDES JOSEFINA TORRES GALVEZ
3rd rowMERCEDES JOSEFINA TORRES GALVEZ
4th rowRUDY ADOLFO TOT OCH
5th rowRUDY ADOLFO TOT OCH
ValueCountFrequency (%)
de 1596
 
5.7%
martinez 543
 
2.0%
gonzalez 444
 
1.6%
leon 424
 
1.5%
lopez 396
 
1.4%
morales 373
 
1.3%
carlos 368
 
1.3%
juan 353
 
1.3%
humberto 310
 
1.1%
hernandez 299
 
1.1%
Other values (866) 22676
81.6%
2023-07-31T16:28:00.931040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 24041
12.7%
21379
11.3%
E 18057
 
9.5%
R 15128
 
8.0%
O 14004
 
7.4%
I 12847
 
6.8%
L 11894
 
6.3%
N 11046
 
5.8%
S 7792
 
4.1%
D 6550
 
3.5%
Other values (19) 46638
24.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 167867
88.6%
Space Separator 21379
 
11.3%
Dash Punctuation 124
 
0.1%
Other Punctuation 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 24041
14.3%
E 18057
10.8%
R 15128
 
9.0%
O 14004
 
8.3%
I 12847
 
7.7%
L 11894
 
7.1%
N 11046
 
6.6%
S 7792
 
4.6%
D 6550
 
3.9%
C 6273
 
3.7%
Other values (16) 40235
24.0%
Space Separator
ValueCountFrequency (%)
21379
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 124
100.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 167867
88.6%
Common 21509
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 24041
14.3%
E 18057
10.8%
R 15128
 
9.0%
O 14004
 
8.3%
I 12847
 
7.7%
L 11894
 
7.1%
N 11046
 
6.6%
S 7792
 
4.6%
D 6550
 
3.9%
C 6273
 
3.7%
Other values (16) 40235
24.0%
Common
ValueCountFrequency (%)
21379
99.4%
- 124
 
0.6%
. 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 24041
12.7%
21379
11.3%
E 18057
 
9.5%
R 15128
 
8.0%
O 14004
 
7.4%
I 12847
 
6.8%
L 11894
 
6.3%
N 11046
 
5.8%
S 7792
 
4.1%
D 6550
 
3.5%
Other values (19) 46638
24.6%

DIRECTOR
Text

MISSING 

Distinct3138
Distinct (%)60.9%
Missing1413
Missing (%)21.5%
Memory size51.4 KiB
2023-07-31T16:28:01.180746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length55
Median length47
Mean length28.889385
Min length8

Characters and Unicode

Total characters148867
Distinct characters33
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2102 ?
Unique (%)40.8%

Sample

1st rowJULIO CESAR VILLELA AMADO
2nd rowVIRGINA SOLANO SERRANO
3rd rowHECOTR WALDEMAR TOT COY
4th rowLUIS FERNANDO SOTO
5th rowMERCEDES QUIROS QUIROS
ValueCountFrequency (%)
de 1033
 
4.7%
lopez 419
 
1.9%
maria 284
 
1.3%
garcia 264
 
1.2%
morales 245
 
1.1%
hernandez 226
 
1.0%
perez 191
 
0.9%
gonzalez 172
 
0.8%
jose 159
 
0.7%
martinez 154
 
0.7%
Other values (2900) 18734
85.6%
2023-07-31T16:28:01.559538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 19876
13.4%
16728
11.2%
E 14329
 
9.6%
R 11929
 
8.0%
O 10562
 
7.1%
I 10091
 
6.8%
L 9240
 
6.2%
N 8673
 
5.8%
S 6055
 
4.1%
D 5449
 
3.7%
Other values (23) 35935
24.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 131998
88.7%
Space Separator 16728
 
11.2%
Dash Punctuation 70
 
< 0.1%
Other Punctuation 69
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 19876
15.1%
E 14329
10.9%
R 11929
 
9.0%
O 10562
 
8.0%
I 10091
 
7.6%
L 9240
 
7.0%
N 8673
 
6.6%
S 6055
 
4.6%
D 5449
 
4.1%
C 4782
 
3.6%
Other values (16) 31012
23.5%
Other Punctuation
ValueCountFrequency (%)
. 65
94.2%
" 2
 
2.9%
, 2
 
2.9%
Space Separator
ValueCountFrequency (%)
16728
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 70
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 131998
88.7%
Common 16869
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 19876
15.1%
E 14329
10.9%
R 11929
 
9.0%
O 10562
 
8.0%
I 10091
 
7.6%
L 9240
 
7.0%
N 8673
 
6.6%
S 6055
 
4.6%
D 5449
 
4.1%
C 4782
 
3.6%
Other values (16) 31012
23.5%
Common
ValueCountFrequency (%)
16728
99.2%
- 70
 
0.4%
. 65
 
0.4%
" 2
 
< 0.1%
, 2
 
< 0.1%
( 1
 
< 0.1%
) 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 148867
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 19876
13.4%
16728
11.2%
E 14329
 
9.6%
R 11929
 
8.0%
O 10562
 
7.1%
I 10091
 
6.8%
L 9240
 
6.2%
N 8673
 
5.8%
S 6055
 
4.1%
D 5449
 
3.7%
Other values (23) 35935
24.1%

NIVEL
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
DIVERSIFICADO
6566 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters85358
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDIVERSIFICADO
2nd rowDIVERSIFICADO
3rd rowDIVERSIFICADO
4th rowDIVERSIFICADO
5th rowDIVERSIFICADO

Common Values

ValueCountFrequency (%)
DIVERSIFICADO 6566
100.0%

Length

2023-07-31T16:28:01.704707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T16:28:01.882432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
diversificado 6566
100.0%

Most occurring characters

ValueCountFrequency (%)
I 19698
23.1%
D 13132
15.4%
V 6566
 
7.7%
E 6566
 
7.7%
R 6566
 
7.7%
S 6566
 
7.7%
F 6566
 
7.7%
C 6566
 
7.7%
A 6566
 
7.7%
O 6566
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 85358
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 19698
23.1%
D 13132
15.4%
V 6566
 
7.7%
E 6566
 
7.7%
R 6566
 
7.7%
S 6566
 
7.7%
F 6566
 
7.7%
C 6566
 
7.7%
A 6566
 
7.7%
O 6566
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 85358
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 19698
23.1%
D 13132
15.4%
V 6566
 
7.7%
E 6566
 
7.7%
R 6566
 
7.7%
S 6566
 
7.7%
F 6566
 
7.7%
C 6566
 
7.7%
A 6566
 
7.7%
O 6566
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 19698
23.1%
D 13132
15.4%
V 6566
 
7.7%
E 6566
 
7.7%
R 6566
 
7.7%
S 6566
 
7.7%
F 6566
 
7.7%
C 6566
 
7.7%
A 6566
 
7.7%
O 6566
 
7.7%

SECTOR
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
PRIVADO
5723 
OFICIAL
637 
COOPERATIVA
 
112
MUNICIPAL
 
94

Length

Max length11
Median length7
Mean length7.0968626
Min length7

Characters and Unicode

Total characters46598
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRIVADO
2nd rowPRIVADO
3rd rowPRIVADO
4th rowOFICIAL
5th rowOFICIAL

Common Values

ValueCountFrequency (%)
PRIVADO 5723
87.2%
OFICIAL 637
 
9.7%
COOPERATIVA 112
 
1.7%
MUNICIPAL 94
 
1.4%

Length

2023-07-31T16:28:01.972661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T16:28:02.085050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
privado 5723
87.2%
oficial 637
 
9.7%
cooperativa 112
 
1.7%
municipal 94
 
1.4%

Most occurring characters

ValueCountFrequency (%)
I 7297
15.7%
A 6678
14.3%
O 6584
14.1%
P 5929
12.7%
R 5835
12.5%
V 5835
12.5%
D 5723
12.3%
C 843
 
1.8%
L 731
 
1.6%
F 637
 
1.4%
Other values (5) 506
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 46598
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 7297
15.7%
A 6678
14.3%
O 6584
14.1%
P 5929
12.7%
R 5835
12.5%
V 5835
12.5%
D 5723
12.3%
C 843
 
1.8%
L 731
 
1.6%
F 637
 
1.4%
Other values (5) 506
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 46598
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 7297
15.7%
A 6678
14.3%
O 6584
14.1%
P 5929
12.7%
R 5835
12.5%
V 5835
12.5%
D 5723
12.3%
C 843
 
1.8%
L 731
 
1.6%
F 637
 
1.4%
Other values (5) 506
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 7297
15.7%
A 6678
14.3%
O 6584
14.1%
P 5929
12.7%
R 5835
12.5%
V 5835
12.5%
D 5723
12.3%
C 843
 
1.8%
L 731
 
1.6%
F 637
 
1.4%
Other values (5) 506
 
1.1%

AREA
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
URBANA
5487 
RURAL
1078 
SIN ESPECIFICAR
 
1

Length

Max length15
Median length6
Mean length5.8371916
Min length5

Characters and Unicode

Total characters38327
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowURBANA
2nd rowURBANA
3rd rowURBANA
4th rowURBANA
5th rowURBANA

Common Values

ValueCountFrequency (%)
URBANA 5487
83.6%
RURAL 1078
 
16.4%
SIN ESPECIFICAR 1
 
< 0.1%

Length

2023-07-31T16:28:02.189976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T16:28:02.305391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
urbana 5487
83.6%
rural 1078
 
16.4%
sin 1
 
< 0.1%
especificar 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 12053
31.4%
R 7644
19.9%
U 6565
17.1%
N 5488
14.3%
B 5487
14.3%
L 1078
 
2.8%
I 3
 
< 0.1%
S 2
 
< 0.1%
E 2
 
< 0.1%
C 2
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 38326
> 99.9%
Space Separator 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 12053
31.4%
R 7644
19.9%
U 6565
17.1%
N 5488
14.3%
B 5487
14.3%
L 1078
 
2.8%
I 3
 
< 0.1%
S 2
 
< 0.1%
E 2
 
< 0.1%
C 2
 
< 0.1%
Other values (2) 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38326
> 99.9%
Common 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 12053
31.4%
R 7644
19.9%
U 6565
17.1%
N 5488
14.3%
B 5487
14.3%
L 1078
 
2.8%
I 3
 
< 0.1%
S 2
 
< 0.1%
E 2
 
< 0.1%
C 2
 
< 0.1%
Other values (2) 2
 
< 0.1%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38327
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 12053
31.4%
R 7644
19.9%
U 6565
17.1%
N 5488
14.3%
B 5487
14.3%
L 1078
 
2.8%
I 3
 
< 0.1%
S 2
 
< 0.1%
E 2
 
< 0.1%
C 2
 
< 0.1%
Other values (3) 3
 
< 0.1%

STATUS
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
ABIERTA
4618 
CERRADA TEMPORALMENTE
1844 
TEMPORAL TITULOS
 
102
TEMPORAL NOMBRAMIENTO
 
2

Length

Max length21
Median length7
Mean length11.075845
Min length7

Characters and Unicode

Total characters72724
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowABIERTA
2nd rowABIERTA
3rd rowABIERTA
4th rowABIERTA
5th rowABIERTA

Common Values

ValueCountFrequency (%)
ABIERTA 4618
70.3%
CERRADA TEMPORALMENTE 1844
 
28.1%
TEMPORAL TITULOS 102
 
1.6%
TEMPORAL NOMBRAMIENTO 2
 
< 0.1%

Length

2023-07-31T16:28:02.401809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T16:28:02.513875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
abierta 4618
54.2%
cerrada 1844
 
21.7%
temporalmente 1844
 
21.7%
temporal 104
 
1.2%
titulos 102
 
1.2%
nombramiento 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 14874
20.5%
E 12100
16.6%
R 10256
14.1%
T 8616
11.8%
I 4722
 
6.5%
B 4620
 
6.4%
M 3796
 
5.2%
O 2054
 
2.8%
L 2050
 
2.8%
1948
 
2.7%
Other values (6) 7688
10.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 70776
97.3%
Space Separator 1948
 
2.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 14874
21.0%
E 12100
17.1%
R 10256
14.5%
T 8616
12.2%
I 4722
 
6.7%
B 4620
 
6.5%
M 3796
 
5.4%
O 2054
 
2.9%
L 2050
 
2.9%
P 1948
 
2.8%
Other values (5) 5740
 
8.1%
Space Separator
ValueCountFrequency (%)
1948
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70776
97.3%
Common 1948
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 14874
21.0%
E 12100
17.1%
R 10256
14.5%
T 8616
12.2%
I 4722
 
6.7%
B 4620
 
6.5%
M 3796
 
5.4%
O 2054
 
2.9%
L 2050
 
2.9%
P 1948
 
2.8%
Other values (5) 5740
 
8.1%
Common
ValueCountFrequency (%)
1948
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 14874
20.5%
E 12100
16.6%
R 10256
14.1%
T 8616
11.8%
I 4722
 
6.5%
B 4620
 
6.4%
M 3796
 
5.2%
O 2054
 
2.8%
L 2050
 
2.8%
1948
 
2.7%
Other values (6) 7688
10.6%

MODALIDAD
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
MONOLINGUE
6405 
BILINGUE
 
161

Length

Max length10
Median length10
Mean length9.9509595
Min length8

Characters and Unicode

Total characters65338
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMONOLINGUE
2nd rowMONOLINGUE
3rd rowMONOLINGUE
4th rowMONOLINGUE
5th rowBILINGUE

Common Values

ValueCountFrequency (%)
MONOLINGUE 6405
97.5%
BILINGUE 161
 
2.5%

Length

2023-07-31T16:28:02.620780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T16:28:02.730338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
monolingue 6405
97.5%
bilingue 161
 
2.5%

Most occurring characters

ValueCountFrequency (%)
N 12971
19.9%
O 12810
19.6%
I 6727
10.3%
L 6566
10.0%
G 6566
10.0%
U 6566
10.0%
E 6566
10.0%
M 6405
9.8%
B 161
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 65338
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 12971
19.9%
O 12810
19.6%
I 6727
10.3%
L 6566
10.0%
G 6566
10.0%
U 6566
10.0%
E 6566
10.0%
M 6405
9.8%
B 161
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 65338
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 12971
19.9%
O 12810
19.6%
I 6727
10.3%
L 6566
10.0%
G 6566
10.0%
U 6566
10.0%
E 6566
10.0%
M 6405
9.8%
B 161
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65338
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 12971
19.9%
O 12810
19.6%
I 6727
10.3%
L 6566
10.0%
G 6566
10.0%
U 6566
10.0%
E 6566
10.0%
M 6405
9.8%
B 161
 
0.2%

JORNADA
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
DOBLE
2208 
MATUTINA
1790 
VESPERTINA
1647 
SIN JORNADA
636 
NOCTURNA
 
210

Length

Max length11
Median length10
Mean length7.8062747
Min length5

Characters and Unicode

Total characters51256
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMATUTINA
2nd rowMATUTINA
3rd rowMATUTINA
4th rowMATUTINA
5th rowVESPERTINA

Common Values

ValueCountFrequency (%)
DOBLE 2208
33.6%
MATUTINA 1790
27.3%
VESPERTINA 1647
25.1%
SIN JORNADA 636
 
9.7%
NOCTURNA 210
 
3.2%
INTERMEDIA 75
 
1.1%

Length

2023-07-31T16:28:02.851439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T16:28:02.981201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
doble 2208
30.7%
matutina 1790
24.9%
vespertina 1647
22.9%
sin 636
 
8.8%
jornada 636
 
8.8%
nocturna 210
 
2.9%
intermedia 75
 
1.0%

Most occurring characters

ValueCountFrequency (%)
A 6784
13.2%
E 5652
11.0%
T 5512
10.8%
N 5204
10.2%
I 4223
 
8.2%
O 3054
 
6.0%
D 2919
 
5.7%
R 2568
 
5.0%
S 2283
 
4.5%
L 2208
 
4.3%
Other values (8) 10849
21.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 50620
98.8%
Space Separator 636
 
1.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 6784
13.4%
E 5652
11.2%
T 5512
10.9%
N 5204
10.3%
I 4223
8.3%
O 3054
 
6.0%
D 2919
 
5.8%
R 2568
 
5.1%
S 2283
 
4.5%
L 2208
 
4.4%
Other values (7) 10213
20.2%
Space Separator
ValueCountFrequency (%)
636
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50620
98.8%
Common 636
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 6784
13.4%
E 5652
11.2%
T 5512
10.9%
N 5204
10.3%
I 4223
8.3%
O 3054
 
6.0%
D 2919
 
5.8%
R 2568
 
5.1%
S 2283
 
4.5%
L 2208
 
4.4%
Other values (7) 10213
20.2%
Common
ValueCountFrequency (%)
636
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 6784
13.2%
E 5652
11.0%
T 5512
10.8%
N 5204
10.2%
I 4223
 
8.2%
O 3054
 
6.0%
D 2919
 
5.7%
R 2568
 
5.0%
S 2283
 
4.5%
L 2208
 
4.3%
Other values (8) 10849
21.2%

PLAN
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
DIARIO(REGULAR)
3925 
FIN DE SEMANA
1773 
SEMIPRESENCIAL (FIN DE SEMANA)
 
307
SEMIPRESENCIAL (UN DIA A LA SEMANA)
 
258
A DISTANCIA
 
105
Other values (8)
 
198

Length

Max length37
Median length15
Mean length16.011422
Min length5

Characters and Unicode

Total characters105131
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDIARIO(REGULAR)
2nd rowDIARIO(REGULAR)
3rd rowDIARIO(REGULAR)
4th rowDIARIO(REGULAR)
5th rowDIARIO(REGULAR)

Common Values

ValueCountFrequency (%)
DIARIO(REGULAR) 3925
59.8%
FIN DE SEMANA 1773
27.0%
SEMIPRESENCIAL (FIN DE SEMANA) 307
 
4.7%
SEMIPRESENCIAL (UN DIA A LA SEMANA) 258
 
3.9%
A DISTANCIA 105
 
1.6%
SEMIPRESENCIAL 68
 
1.0%
SEMIPRESENCIAL (DOS DIAS A LA SEMANA) 51
 
0.8%
SABATINO 34
 
0.5%
VIRTUAL A DISTANCIA 30
 
0.5%
DOMINICAL 9
 
0.1%
Other values (3) 6
 
0.1%

Length

2023-07-31T16:28:03.115979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
diario(regular 3925
30.8%
semana 2389
18.7%
fin 2080
16.3%
de 2080
16.3%
semipresencial 684
 
5.4%
a 444
 
3.5%
la 309
 
2.4%
un 258
 
2.0%
dia 258
 
2.0%
distancia 135
 
1.1%
Other values (8) 181
 
1.4%

Most occurring characters

ValueCountFrequency (%)
A 14757
14.0%
R 12497
11.9%
I 11965
11.4%
E 10450
9.9%
D 6511
 
6.2%
6177
 
5.9%
N 5591
 
5.3%
L 4961
 
4.7%
( 4541
 
4.3%
) 4541
 
4.3%
Other values (12) 23140
22.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 89872
85.5%
Space Separator 6177
 
5.9%
Open Punctuation 4541
 
4.3%
Close Punctuation 4541
 
4.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 14757
16.4%
R 12497
13.9%
I 11965
13.3%
E 10450
11.6%
D 6511
7.2%
N 5591
 
6.2%
L 4961
 
5.5%
U 4215
 
4.7%
S 4028
 
4.5%
O 4023
 
4.5%
Other values (9) 10874
12.1%
Space Separator
ValueCountFrequency (%)
6177
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4541
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4541
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 89872
85.5%
Common 15259
 
14.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 14757
16.4%
R 12497
13.9%
I 11965
13.3%
E 10450
11.6%
D 6511
7.2%
N 5591
 
6.2%
L 4961
 
5.5%
U 4215
 
4.7%
S 4028
 
4.5%
O 4023
 
4.5%
Other values (9) 10874
12.1%
Common
ValueCountFrequency (%)
6177
40.5%
( 4541
29.8%
) 4541
29.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105131
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 14757
14.0%
R 12497
11.9%
I 11965
11.4%
E 10450
9.9%
D 6511
 
6.2%
6177
 
5.9%
N 5591
 
5.3%
L 4961
 
4.7%
( 4541
 
4.3%
) 4541
 
4.3%
Other values (12) 23140
22.0%

DEPARTAMENTAL
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.4 KiB
GUATEMALA NORTE
1037 
GUATEMALA SUR
796 
GUATEMALA OCCIDENTE
774 
ESCUINTLA
599 
HUEHUETENANGO
495 
Other values (12)
2865 

Length

Max length19
Median length15
Mean length12.728602
Min length6

Characters and Unicode

Total characters83576
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALTA VERAPAZ
2nd rowALTA VERAPAZ
3rd rowALTA VERAPAZ
4th rowALTA VERAPAZ
5th rowALTA VERAPAZ

Common Values

ValueCountFrequency (%)
GUATEMALA NORTE 1037
15.8%
GUATEMALA SUR 796
12.1%
GUATEMALA OCCIDENTE 774
11.8%
ESCUINTLA 599
9.1%
HUEHUETENANGO 495
7.5%
SUCHITEPEQUEZ 377
 
5.7%
GUATEMALA ORIENTE 363
 
5.5%
IZABAL 360
 
5.5%
CHIMALTENANGO 349
 
5.3%
ALTA VERAPAZ 348
 
5.3%
Other values (7) 1068
16.3%

Length

2023-07-31T16:28:03.212322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
guatemala 2970
29.3%
norte 1037
 
10.2%
sur 796
 
7.9%
occidente 774
 
7.6%
escuintla 599
 
5.9%
huehuetenango 495
 
4.9%
verapaz 468
 
4.6%
suchitepequez 377
 
3.7%
oriente 363
 
3.6%
izabal 360
 
3.6%
Other values (10) 1887
18.6%

Most occurring characters

ValueCountFrequency (%)
A 15018
18.0%
E 10557
12.6%
T 7812
9.3%
U 6773
 
8.1%
L 5069
 
6.1%
N 4641
 
5.6%
G 3936
 
4.7%
I 3576
 
4.3%
3560
 
4.3%
M 3491
 
4.2%
Other values (12) 19143
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 80016
95.7%
Space Separator 3560
 
4.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15018
18.8%
E 10557
13.2%
T 7812
9.8%
U 6773
8.5%
L 5069
 
6.3%
N 4641
 
5.8%
G 3936
 
4.9%
I 3576
 
4.5%
M 3491
 
4.4%
O 3442
 
4.3%
Other values (11) 15701
19.6%
Space Separator
ValueCountFrequency (%)
3560
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 80016
95.7%
Common 3560
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15018
18.8%
E 10557
13.2%
T 7812
9.8%
U 6773
8.5%
L 5069
 
6.3%
N 4641
 
5.8%
G 3936
 
4.9%
I 3576
 
4.5%
M 3491
 
4.4%
O 3442
 
4.3%
Other values (11) 15701
19.6%
Common
ValueCountFrequency (%)
3560
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15018
18.0%
E 10557
12.6%
T 7812
9.3%
U 6773
 
8.1%
L 5069
 
6.1%
N 4641
 
5.6%
G 3936
 
4.7%
I 3576
 
4.3%
3560
 
4.3%
M 3491
 
4.2%
Other values (12) 19143
22.9%

CODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLAN DEPARTA
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6566
Missing (%)100.0%
Memory size51.4 KiB

CODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLAN
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6566
Missing (%)100.0%
Memory size51.4 KiB

CODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLAN
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6566
Missing (%)100.0%
Memory size51.4 KiB

CODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLAN DE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6566
Missing (%)100.0%
Memory size51.4 KiB

ZONA
Categorical

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)1.4%
Missing5030
Missing (%)76.6%
Memory size51.4 KiB
ZONA 1
628 
ZONA 7
173 
ZONA 12
114 
ZONA 18
102 
ZONA 6
71 
Other values (16)
448 

Length

Max length7
Median length6
Mean length6.3255208
Min length6

Characters and Unicode

Total characters9716
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZONA 1
2nd rowZONA 1
3rd rowZONA 1
4th rowZONA 1
5th rowZONA 1

Common Values

ValueCountFrequency (%)
ZONA 1 628
 
9.6%
ZONA 7 173
 
2.6%
ZONA 12 114
 
1.7%
ZONA 18 102
 
1.6%
ZONA 6 71
 
1.1%
ZONA 11 62
 
0.9%
ZONA 2 54
 
0.8%
ZONA 19 53
 
0.8%
ZONA 13 46
 
0.7%
ZONA 3 40
 
0.6%
Other values (11) 193
 
2.9%
(Missing) 5030
76.6%

Length

2023-07-31T16:28:03.306558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
zona 1536
50.0%
1 628
20.4%
7 173
 
5.6%
12 114
 
3.7%
18 102
 
3.3%
6 71
 
2.3%
11 62
 
2.0%
2 54
 
1.8%
19 53
 
1.7%
13 46
 
1.5%
Other values (12) 233
 
7.6%

Most occurring characters

ValueCountFrequency (%)
Z 1536
15.8%
O 1536
15.8%
N 1536
15.8%
A 1536
15.8%
1536
15.8%
1 1188
12.2%
2 202
 
2.1%
7 193
 
2.0%
8 107
 
1.1%
6 89
 
0.9%
Other values (5) 257
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6144
63.2%
Decimal Number 2036
 
21.0%
Space Separator 1536
 
15.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1188
58.3%
2 202
 
9.9%
7 193
 
9.5%
8 107
 
5.3%
6 89
 
4.4%
3 86
 
4.2%
9 81
 
4.0%
5 44
 
2.2%
0 27
 
1.3%
4 19
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
Z 1536
25.0%
O 1536
25.0%
N 1536
25.0%
A 1536
25.0%
Space Separator
ValueCountFrequency (%)
1536
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6144
63.2%
Common 3572
36.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1536
43.0%
1 1188
33.3%
2 202
 
5.7%
7 193
 
5.4%
8 107
 
3.0%
6 89
 
2.5%
3 86
 
2.4%
9 81
 
2.3%
5 44
 
1.2%
0 27
 
0.8%
Latin
ValueCountFrequency (%)
Z 1536
25.0%
O 1536
25.0%
N 1536
25.0%
A 1536
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Z 1536
15.8%
O 1536
15.8%
N 1536
15.8%
A 1536
15.8%
1536
15.8%
1 1188
12.2%
2 202
 
2.1%
7 193
 
2.0%
8 107
 
1.1%
6 89
 
0.9%
Other values (5) 257
 
2.6%

Interactions

2023-07-31T16:27:51.179511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-07-31T16:28:03.387848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Unnamed: 0DEPARTAMENTOSECTORAREASTATUSMODALIDADJORNADAPLANDEPARTAMENTALZONA
Unnamed: 01.0000.8500.0850.1580.1040.1090.1240.1190.8820.977
DEPARTAMENTO0.8501.0000.1460.1860.1250.2960.1250.1071.0001.000
SECTOR0.0850.1461.0000.1290.0710.1130.1310.1260.1500.225
AREA0.1580.1860.1291.0000.0350.0900.0770.0670.2050.265
STATUS0.1040.1250.0710.0351.0000.0210.1640.1390.1340.126
MODALIDAD0.1090.2960.1130.0900.0211.0000.0920.0840.2950.000
JORNADA0.1240.1250.1310.0770.1640.0921.0000.5600.1340.093
PLAN0.1190.1070.1260.0670.1390.0840.5601.0000.1160.065
DEPARTAMENTAL0.8821.0000.1500.2050.1340.2950.1340.1161.0000.994
ZONA0.9771.0000.2250.2650.1260.0000.0930.0650.9941.000

Missing values

2023-07-31T16:27:51.540036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-31T16:27:52.284502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-31T16:27:53.001852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0CODIGODISTRITODEPARTAMENTOMUNICIPIOESTABLECIMIENTODIRECCIONTELEFONOSUPERVISORDIRECTORNIVELSECTORAREASTATUSMODALIDADJORNADAPLANDEPARTAMENTALCODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLAN DEPARTACODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLANCODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLANCODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLAN DEZONA
0016-01-0138-4616-031ALTA VERAPAZCOBANCOLEGIO COBANKM.2 SALIDA A SAN JUAN CHAMELCO ZONA 877945104MERCEDES JOSEFINA TORRES GALVEZJULIO CESAR VILLELA AMADODIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEMATUTINADIARIO(REGULAR)ALTA VERAPAZNaNNaNNaNNaNNaN
1116-01-0139-4616-031ALTA VERAPAZCOBANCOLEGIO PARTICULAR MIXTO VERAPAZKM 209.5 ENTRADA A LA CIUDAD77367402MERCEDES JOSEFINA TORRES GALVEZNaNDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEMATUTINADIARIO(REGULAR)ALTA VERAPAZNaNNaNNaNNaNNaN
2216-01-0140-4616-031ALTA VERAPAZCOBANCOLEGIO "LA INMACULADA"7A. AVENIDA 11-109 ZONA 678232301MERCEDES JOSEFINA TORRES GALVEZVIRGINA SOLANO SERRANODIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEMATUTINADIARIO(REGULAR)ALTA VERAPAZNaNNaNNaNNaNNaN
3316-01-0141-4616-005ALTA VERAPAZCOBANESCUELA NACIONAL DE CIENCIAS COMERCIALES2A CALLE 11-10 ZONA 279514215RUDY ADOLFO TOT OCHNaNDIVERSIFICADOOFICIALURBANAABIERTAMONOLINGUEMATUTINADIARIO(REGULAR)ALTA VERAPAZNaNNaNNaNNaNNaN
4416-01-0142-4616-005ALTA VERAPAZCOBANINSTITUTO NORMAL MIXTO DEL NORTE 'EMILIO ROSALES PONCE'3A AVE 6-23 ZONA 1179521468RUDY ADOLFO TOT OCHNaNDIVERSIFICADOOFICIALURBANAABIERTABILINGUEVESPERTINADIARIO(REGULAR)ALTA VERAPAZNaNNaNNaNNaNNaN
5516-01-0143-4616-031ALTA VERAPAZCOBANCOLEGIO PARTICULAR MIXTO IMPERIAL5A. CALLE 1-9 ZONA 357101061MERCEDES JOSEFINA TORRES GALVEZHECOTR WALDEMAR TOT COYDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEDOBLEFIN DE SEMANAALTA VERAPAZNaNNaNNaNNaNNaN
6616-01-0145-4616-006ALTA VERAPAZCOBANINSTITUTO DE TURSMO Y AVIACON DEL NORTE I.T.A.N3 AV. 5-28 ZONA 454641454EFRAIN CAAL CUCLUIS FERNANDO SOTODIVERSIFICADOPRIVADOURBANACERRADA TEMPORALMENTEMONOLINGUEMATUTINADIARIO(REGULAR)ALTA VERAPAZNaNNaNNaNNaNNaN
7716-01-0147-4616-031ALTA VERAPAZCOBANCOLEGIO "LA INMACULADA"7A. CALLE 11-09 ZONA 6 COBAN49532425MERCEDES JOSEFINA TORRES GALVEZMERCEDES QUIROS QUIROSDIVERSIFICADOPRIVADORURALCERRADA TEMPORALMENTEMONOLINGUEDOBLEDIARIO(REGULAR)ALTA VERAPAZNaNNaNNaNNaNNaN
8816-01-0150-4616-006ALTA VERAPAZCOBANINSTITUTO INTERCULTRUAL ALTAVERAPACENCESE -IIAV-3A. AVAENIDA 1-23 ZONA 4NaNEFRAIN CAAL CUCGUILLERMO ESTUARDO VASQUEZ MORALESDIVERSIFICADOPRIVADOURBANACERRADA TEMPORALMENTEBILINGUEDOBLEFIN DE SEMANAALTA VERAPAZNaNNaNNaNNaNNaN
9916-01-0155-4616-031ALTA VERAPAZCOBANLICEO "MODERNO LATINO"11 AVENIDA 5-17 ZONA 479522555MERCEDES JOSEFINA TORRES GALVEZJORGE BENEDICTO COC POPDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEDOBLEFIN DE SEMANAALTA VERAPAZNaNNaNNaNNaNNaN
Unnamed: 0CODIGODISTRITODEPARTAMENTOMUNICIPIOESTABLECIMIENTODIRECCIONTELEFONOSUPERVISORDIRECTORNIVELSECTORAREASTATUSMODALIDADJORNADAPLANDEPARTAMENTALCODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLAN DEPARTACODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLANCODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLANCODIGO DISTRITO DEPARTAMENTO MUNICIPIO ESTABLECIMIENTO DIRECCION TELEFONO SUPERVISOR DIRECTOR NIVEL SECTOR AREA STATUS MODALIDAD JORNADA PLAN DEZONA
65561597419-08-0011-4619-013ZACAPASAN DIEGOINSTITUTO NACIONAL DE EDUCACION DIVERSIFICADABARRIO EL CENTRONaNDOUGLAS DONALDO URRUTIA MATEOVIVIAN ILIANA MIRANDA DIAZDIVERSIFICADOOFICIALURBANAABIERTAMONOLINGUENOCTURNADIARIO(REGULAR)ZACAPANaNNaNNaNNaNNaN
65571597519-08-0890-4619-013ZACAPASAN DIEGOINSTITUTO DIVERSIFICADO POR COOPERATIVA PROF. CARLOS ROBERTO DONIS OSORIOBARRIO EL CENTRONaNDOUGLAS DONALDO URRUTIA MATEOLEONEL AUGUSTO LEMUS MOSCOSODIVERSIFICADOCOOPERATIVAURBANAABIERTAMONOLINGUEVESPERTINADIARIO(REGULAR)ZACAPANaNNaNNaNNaNNaN
65581597619-09-0008-4619-014ZACAPALA UNIONINSTITUTO NACIONAL DE EDUCACION DIVERSIFICADABARRIO NUEVONaNWILBER OBDULIO MEJIA SUCHITEGUSTAVO LEIVA MORALESDIVERSIFICADOOFICIALURBANAABIERTAMONOLINGUEVESPERTINADIARIO(REGULAR)ZACAPANaNNaNNaNNaNNaN
65591597719-09-0034-4619-021ZACAPALA UNIONLICEO PARTICULAR MIXTO "JIREH"BARRIO NUEVONaNBERTA ALICIA LEIVA CORDON DE GARCIAANA MARIA CUELLAR GUERRA DE RAMIREZDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEDOBLEFIN DE SEMANAZACAPANaNNaNNaNNaNNaN
65601597819-09-0037-4619-021ZACAPALA UNIONLICEO PARTICULAR MIXTO "JIREH"BARRIO NUEVONaNBERTA ALICIA LEIVA CORDON DE GARCIAANA MARIA CUELLAR GUERRA DE RAMIREZDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEDOBLEDIARIO(REGULAR)ZACAPANaNNaNNaNNaNNaN
65611597919-09-0040-4619-021ZACAPALA UNIONLICEO PARTICULAR MIXTO "JIREH"BARRIO NUEVONaNBERTA ALICIA LEIVA CORDON DE GARCIAANA MARIA CUELLAR GUERRA DE RAMIREZDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEMATUTINADIARIO(REGULAR)ZACAPANaNNaNNaNNaNNaN
65621598019-09-0048-4619-021ZACAPALA UNIONLICEO PARTICULAR MIXTO " JIREH"BARRIO NUEVONaNBERTA ALICIA LEIVA CORDON DE GARCIAANA MARIA CUELLAR GUERRA DE RAMIREZDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUESIN JORNADASEMIPRESENCIAL (UN DIA A LA SEMANA)ZACAPANaNNaNNaNNaNNaN
65631598119-10-0013-4619-015ZACAPAHUITEINSTITUTO DIVERSIFICADOBARRIO BUENOS AIRESNaNYADIRA FERNANDA SOSA GUERRAMARLON JOSUE ARCHILA LORENZODIVERSIFICADOOFICIALURBANAABIERTAMONOLINGUENOCTURNADIARIO(REGULAR)ZACAPANaNNaNNaNNaNNaN
65641598219-10-1009-4619-015ZACAPAHUITEINSTITUTO MIXTO DE EDUCACION DIVERSIFICADA POR COOPERATIVA DE ENSENANZABARRIO EL CAMPONaNYADIRA FERNANDA SOSA GUERRAROBIDIO PORTILLO SALGUERODIVERSIFICADOCOOPERATIVAURBANAABIERTAMONOLINGUEVESPERTINADIARIO(REGULAR)ZACAPANaNNaNNaNNaNNaN
65651598319-11-0018-4619-020ZACAPASAN JORGEINSTITUTO MIXTO DE EDUCACION DIVERSIFICADA POR COOPERATIVABARRIO EL CENTRONaNALBA LUZ MENDEZVICTOR HUGO GUERRA MONROYDIVERSIFICADOCOOPERATIVAURBANAABIERTAMONOLINGUEMATUTINADIARIO(REGULAR)ZACAPANaNNaNNaNNaNNaN